Empirical Algorithm Research Articles

Reconstruction of the Total Solar Irradiance During the Last Millennium

Solar irradiance variations across various timescales, from minutes to centuries, represent a potential natural driver of past regional and global climate cold phases. To accurately assess the Sun’s effect on climate, particularly during periods of exceptionally low solar activity, known as grand minima, an accurate reconstruction of solar forcing is essential. While direct measurements of the total solar irradiance (TSI) only began in the late 1970s, with the advent of space radiometers, indirect evidence from various historical proxies suggests that the Sun’s magnetic activity has undergone possible significant fluctuations over much longer timescales. Employing diverse and independent methods for TSI reconstruction is essential to gaining a comprehensive understanding of this issue. This study employs a semi-empirical model to reconstruct TSI over the past millennium. Our approach uses an estimated open solar magnetic field (F o ), derived from cosmogenic isotope data, as a proxy for solar activity. We reconstruct the cyclic variations of TSI, due to the solar surface magnetic features, by correlating F o with the parameter of active region functional form. We obtain the long-term TSI trend by applying the empirical mode decomposition algorithm to the reconstructed F o to filter out the 11 yr and 22 yr solar variability. We prepare a reconstructed TSI record, spanning 971 to 2020 CE. The estimated departure from modern TSI values occurred during the Spörer minimum (around 1400 CE), with a decrease of approximately 2.3 Wm−2. A slightly smaller decline of 2.2 Wm−2 is reported during the Maunder minimum, between 1645 and 1715 CE.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Pore and fracture pressures prediction — A new geomechanic approach in deepwater salt overthrusts: Case histories from the Gulf of Mexico

Along this active exploration belt, applying the conventional effective stress methods and algorithms, wherein the maximum stress ([Formula: see text]) is vertical and can be attained from the subsurface overburden (OB), would lead to unintended and unrealistic results. In the frontier active thrust belt of the Gulf of Mexico, the unique geomechanical setting of [Formula: see text] as the lateral salt creep and the minimum stress ([Formula: see text]) as the OB greatly impact the formation geopressure framework. The buoyancy of thick salt produces two different pressure gradients (PGs) above and below the salt. Moreover, the inclusion of rafted sediments in the salt and the plowing rubble zone at the salt base substantially affects the pore and fracture pressures (PP-FP) profiles. These proceeding geologic settings are the foundation for the conceptual framework. Building an alternative predrilling prediction numerical model based on these anomalous geomechanical settings and the lack of adequate seismic velocity is a challenge. All the available direct measured or pertained PP-FP data from the key wells are collected and tabulated. Prediction models are established by correlating the populated database and generating the empirical algorithm for each data gather. A substantial discrepancy occurs between above and below the salt sections where high PG in the sediment above the salt and slow PG development below the salt. There is considerable regressive pressure (average 2 ppg) in PP-FP subsalt sections. The PP within the salt is contingent on the presence of sediment inclusions, and a substantial FP drop in the rubble zone leads to the extensive loss of mud circulation. The trend lines of each data gather led to generating two depth-dependent equations for the PP-FP above and within the salt and two others for the subsalt. The prediction models are validated against a blind data set. Before drilling, this model establishes the PP-FP versus the sediment subsea depth in an abnormal geomechanical setting and the lack of coherent vertical seismic velocity for PP-FP predictions.

Bridge modal identification method based on adaptive VMD-HT algorithm

ABSTRACT Bridge engineering structures play a crucial role in transportation. Traditional methods of road and bridge closures for inspection and evaluation, while effective, increase bridge inspection costs and disrupt normal traffic flow. Therefore, a rapid detection and evaluation method for bridge structural modal parameters in the operational state is more suitable for practical engineering applications. However, under real operating conditions, bridge vibration signals typically contain a large amount of noise and interference, and modal identification must overcome issues of non-stationarity and non-linearity in the vibration signals caused by vehicle loads and environmental changes to ensure the accuracy of the identification. This study replaces the traditional Empirical Mode Decomposition algorithm in the Hilbert-Huang Transform with the Variational Mode Decomposition algorithm. It proposes a new method that combines the VMD algorithm with the Hilbert Transform, referred to as the Variational Mode Decomposition-Hilbert Transform method. Compared to the EMD algorithm, it offers significant improvements in suppressing mode mixing and endpoint effects. A vehicle-bridge coupling model was constructed, and the bridge’s natural modal information was successfully extracted from the vibration response signals. The research results indicate that the improved algorithm offers higher accuracy and faster computational speed compared to traditional identification algorithms.

Research of signal processing and detection of satellite telemetry data based on EMD and MBSE

Abstract Based on the characteristics of abnormal data and high Signal-to-Noise Ratio in satellite in-orbit telemetry data, the empirical mode decomposition algorithm is used to remove the abnormal data, and the method is effective. By using the empirical mode decomposition algorithm, trend prediction of telemetry data can also be achieved, and its practicality can be demonstrated through signal construction and development. The combination of the empirical mode decomposition algorithm for telemetry data processing with the satellite’s digital model constructed by MBSE software has expanded the application scope of the digital satellite model.

Capturing the Dynamics of Dissolved Organic Carbon (DOC) in Tidal Saltmarsh Estuaries Using Remote‐Sensing‐Informed Models

AbstractThe fluxes of dissolved organic carbon (DOC) through tidal marsh‐influenced estuaries remain poorly quantified and have been identified as a missing component in carbon‐cycle models. The extreme variability inherent to these ecosystems of the land‐ocean interface challenge our ability to capture DOC‐concentration dynamics and to calculate accurate DOC fluxes. In situ discrete and continuous measurements provide high‐quality estimates of DOC concentration, but these strategies are constrained spatially and temporally and can be costly to operate. Here, field measurements and high‐spatial‐resolution remote sensing were used to train and validate a predictive model of DOC‐concentration distributions in the Plum Island Estuary (PIE), a mesotidal saltmarsh‐influenced estuary in Massachusetts. A large set of field measurements collected between 2017 and 2023 was used to develop and validate an empirical algorithm to retrieve DOC concentration with a ±15% uncertainty from Sentinel‐2 imagery. Implementation on 141 useable images produced a 6‐year time series (2017–2023) of DOC distributions along the thalweg. Analysis of the time series helped identify river discharge, tidal water level (WL), and a marsh enhanced vegetation index 2 as predictors of DOC distribution in the estuary, and facilitated the training and validation of a simple model estimating the distribution. This simple model was able to predict DOC along the PIE thalweg within ±16% of the in situ measurements. Implementation for three years (2020–2022) illustrated how this type of remote‐sensing‐informed models can be coupled with the outputs hydrodynamic models to calculate DOC fluxes in tidal marsh‐influenced estuaries and estimate DOC export to the coastal ocean.

Comparative analysis of empirical decomposition algorithms in predicting tire-pavement friction from asphalt surface textures: a Hilbert–Huang transform (HHT) analysis

The frictional properties of pavement are contingent on its surface texture, which consists of multiple scales that each contribute differently to friction generation at the tire-surface interface. Consequently, this study applied three adaptive decomposition algorithms of the Hilbert–Huang Transformation (HHT) to extract essential texture parameters from surface profiles, aiming to improve understanding of how pavement texture impacts friction properties. The decomposition algorithms used are Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD) and Complete EEMD with Adaptive Noise (CEEMDAN). A Circular Track Meter (CTMeter) was used to measure the surface texture profiles of twelve asphalt mixture slabs with high macrotexture, while friction properties were assessed using a dynamic friction tester (DFT). Additionally, sixteen texture parameters related to height, spacing, and shape were computed to characterize the surface texture of both the original profile and the derived intrinsic mode functions (IMFs). The statistical analysis revealed that while the third IMF (IMF3) obtained by the EEMD exhibited the strongest correlation with the original profile, reaching up to 0.69, the IMFs obtained by EMD demonstrated the highest correlation with the DFT results. Under different polishing conditions and DFT speeds, the friction-texture correlation varied significantly, with the highest correlation observed at the peak values of DFT results and DFT values at 20 km/h (DFT20). Among the texture indicators, in addition to the mean profile depth (MPD), the root mean square (Rq), root mean square wavelength (λq), and two points slope variance (SV2) were recommended for characterizing both the original texture and IMFs profiles in the correlation analysis of the friction-texture relationship.

An Observed Relationship Between Satellite‐Estimated Transmittance and Ground‐Estimated Water Vapor: Implications for High‐Temporal‐Resolution Water Vapor Retrieval From Non‐Geostationary Satellite Measurements

AbstractThe magnitude of integrated water vapor (IWV) varies considerably in the spatial‐temporal domain, which demonstrates the significance of high‐spatiotemporal‐resolution IWV observations for atmospheric water vapor distribution monitoring, both locally and globally. Unlike previously published algorithms based on data fusion, an empirical retrieval algorithm is for the first time proposed to directly retrieve high‐temporal‐resolution IWV estimates from near‐infrared radiance observations of the non‐geostationary Ocean and Land Color Instrument (OLCI). The retrieval algorithm is developed based on an observed regression relationship between satellite‐based OLCI‐estimated transmittance and ground‐based Global Navigation Satellite System (GNSS)‐estimated IWV in the temporal domain. The results show that all newly retrieved IWV estimates have an overall good consistency with ground‐based IWV from additional GNSS‐sensed measurements, indicating the feasibility of the retrieval approach. The performance of the retrieval algorithm is acceptable and satisfactory when compared with that of IWV retrievals listed in previous studies.

Prediction of SO2, NOx and PM in the sintering process based on deep learning

The accurate prediction of SO2, NOx and PM emissions in the iron ore sintering process could adjust the desulfurization and denitrification operation in time. The study presented an integrated prediction model for SO2, NOx and PM in sintering flue gas. Gradient boosting decision tree, recurrent neural network, gated recurrent unit were chosen as sub-models to predict SO2, NOx and PM by comparing different regression prediction models, which were then combined to form an integrated prediction model (MMEP). The box plots, empirical mode decomposition algorithm, Pearson correlation coefficient and maximum information coefficient to select independent variables for the predictive model. The MMEP model had an overall accuracy greater than 0.82, as verified by production data, which could provide guidance for on-site sintering production.

Energy-Efficient Algorithm for Data Path Selection in High-Density Wireless Sensor Networks

Relevance. Increasing the number of sensor devices per unit area consequently reduces the physical distance between the devices in the sensor network. Such networks are usually deployed over a large area and the sensor device that wants to transmit a data packet is located far away from the base station. In such a case, the source device is challenged to choose a transmission path that consumes the least amount of energy resources and satisfies the delivery time requirements. The objective of this study is to develop and validate the effectiveness of an empirical algorithm for selecting a transmission path that reduces the energy consumption of high-density wireless sensor networks. Methods of system analysis, analytical modeling, geometry and probability theories are used. Solution. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. When building data transmission routes, any sensor devices can be used as repeaters. At the same time, the increase in the number of repeaters leads to an increase in the time of data delivery. Novelty. It is assumed that the sensor network is deployed in a limited area and is a set of devices that are connected to each other informationally and energetically. Any sensor devices may be used as repeaters when constructing data transmission routes.Significance (theoretical). Dependences of power consumption level on various system parameters affecting the processes of functioning of high-density wireless sensor networks have been obtained. Significance (practical). The proposed empirical algorithm for selecting a rational data transmission route in a wireless sensor network allows us to determine, among all alternatives, the route to the coordinator that requires the least power. The effectiveness of the proposed empirical power-saving algorithm is confirmed by simulation modeling.

Leveraging Bayesian methods for addressing multi-uncertainty in data-driven seismic liquefaction assessment

When assessing seismic liquefaction potential with data-driven models, addressing the uncertainties of establishing models, interpreting cone penetration tests (CPT) data and decision threshold is crucial for avoiding biased data selection, ameliorating overconfident models, and being flexible to varying practical objectives, especially when the training and testing data are not identically distributed. A workflow characterized by leveraging Bayesian methodology was proposed to address these issues. Employing a Multi-Layer Perceptron (MLP) as the foundational model, this approach was benchmarked against empirical methods and advanced algorithms for its efficacy in simplicity, accuracy, and resistance to overfitting. The analysis revealed that, while MLP models optimized via maximum a posteriori algorithm suffices for straightforward scenarios, Bayesian neural networks showed great potential for preventing overfitting. Additionally, integrating decision thresholds through various evaluative principles offers insights for challenging decisions. Two case studies demonstrate the framework's capacity for nuanced interpretation of in situ data, employing a model committee for a detailed evaluation of liquefaction potential via Monte Carlo simulations and basic statistics. Overall, the proposed step-by-step workflow for analyzing seismic liquefaction incorporates multifold testing and real-world data validation, showing improved robustness against overfitting and greater versatility in addressing practical challenges. This research contributes to the seismic liquefaction assessment field by providing a structured, adaptable methodology for accurate and reliable analysis.

Classification and identification of pest, diseases and nutrient deficiency in paddy using layer based EMD phase features with decision tree

Pest attack, disease incidence, and nutrient deficiency are the major factors limiting the yield of paddy. Therefore, the paper proposes a classification system for the identification of pest, disease, and nutrient deficiency classes. This approach initially preprocesses leaf images using entropy filtering followed by a leaf segmentation process. Multiple layers are then constructed on the leaf image through which features are extracted. The Gray Level Co-occurrence Matrix (GLCM) algorithm and Principal Component Analysis (PCA) are used to extract the global texture features of the leaf image. A 1D-signal sequence is constructed on each layer, which is decomposed by the Empirical Mode Decomposition algorithm from which the phase features are estimated. The features are trained/classified using the decision tree classifiers that classify the pest attack, disease incidence, and nutrient deficiency categories. The proposed approach provides a precision, accuracy, specificity, sensitivity, and F1-score of 97 %, 97.88 %, 96.52 %, 96.7 %, and 96.7 % respectively.

CDOM dynamics in two coastal zones influenced by contrasting land uses in northern Patagonia

Colored dissolved organic matter (CDOM) is an indicator and optical proxy of terrestrial processes such as land use with allochthonous material fluxes, biogeochemical cycles, and water quality in coastal zones influenced by rivers. However, the role of land use changes on the spatial and temporal availability of CDOM has been poorly explored in Chile. Here, we studied two watersheds with similar climates and contrasting land use patterns in northern Patagonia considering the sampling of CDOM in their estuarine and adjacent coastal ocean. An empirical algorithm with the coefficients adjusted to our study areas to estimate CDOM was applied to Landsat 7 and 8 images to examine temporal variability of CDOMest from 2001 to 2011 and 2013–2020. Our results showed an increasing trend of CDOMest in both areas. Different trends in land use patterns between the two watersheds showed a significant correlation with CDOMest and contrasting associations with environmental variables. Higher humification was found in Yaldad in comparison with Colu. In both areas, allochthonous materials predominated, especially during austral spring according to the low values of the Fluorescence Index (FI). Our results highlight the potential of CDOMest to parameterize biogeochemical cycling models and to further understand the dynamics of CDOM in coastal ecosystems.

Empirical algorithms for estimating downwelling light attenuation at λ=490 nm from Sentinel-2 in a narrow optically complex bay, Plymouth Sound, United Kingdom

Empirical algorithms for estimating downwelling light attenuation at λ=490 nm from Sentinel-2 in a narrow optically complex bay, Plymouth Sound, United Kingdom

Identification of cortical arteries and veins in awake mice using two-photon microscopy.

Distinguishing arteries from veins in the cerebral cortex is critical for studying hemodynamics under pathophysiological conditions, which plays an important role in the diagnosis and treatment of various vessel-related diseases. However, due to the complexity of the cerebral vascular network, it is challenging to identify arteries and veins invivo. Here, we demonstrate an artery-vein separation method that employs a combination of multiple scanning modes of two-photon microscopy and a custom-designed stereoscopic fixation device for mice. In this process, we propose a novel method for determining the line scanning direction, which allows us to determine the blood flow directions. The vasculature branches have been identified using an optimized z-stack scanning mode, followed by the separation of blood vessel types according to the directions of blood flow and branching patterns. Using this strategy, the penetrating arterioles and penetrating venules in awake mice could be accurately identified and the type of cerebral thrombus has been also successfully isolated without any empirical knowledge or algorithms. Our research presents a new, more accurate, and efficient method for cortical artery-vein separation in awake mice, providing a useful strategy for the application of two-photon microscopy in the study of cerebrovascular pathophysiology.

Ocean water colour retrieval method based on directional polarimetric camera

ABSTRACT Chlorophyll-a (Chl-a) concentrations serve as a pivotal metric for assessing the biological content and eutrophication levels of waterbodies. In optical remote sensing, empirical and semi-analytical methods are commonly employed for Chl-a retrieval. Leveraging the Level-1B remote sensing data from the Directional Polarimetric Camera (DPC) aboard the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS), this study amalgamates empirical algorithms (OC2, OC3, Aiken-P, and Aiken-C) and look-up-table (LUT) techniques to estimate Chl-a in open water. Test data acquired from DPC on 20 August 2022, are validated against the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra satellite and AERONET-Ocean Color water-leaving radiance data for the same area and date. The results reveal that among the tested algorithms, particularly in the log10 scale space, the Aiken-P algorithm demonstrates the highest correlation coefficient (R2 = 0.6333), with the lowest bias of 1.0366 and a minimum mean absolute error (MAE) of 1.2097. This underscores the suitability and accuracy of the Aiken-P algorithm for water colour retrieval based on DPC-derived data, highlighting its practical applicability.

Design of short-term load forecasting method considering user behavior

In order to ensure optimize the short-term power load forecasting effect, load forecasting method design taking into account the user behavior is studied. Mining user power consumption behavior data from power user power consumption information acquisition system, and suppressing power consumption behavior data noise through empirical mode decomposition algorithm; Input the denoised user behavior data into the improved Relief algorithm, extract the user power consumption behavior characteristics, eliminate redundant features through correlation analysis, and cluster the user behavior characteristics using K-means clustering algorithm to reduce the dimension of the feature data and the complexity of the features, then input the clustered features into the online extreme learning machine (OS-ELM), load forecasting is realized through online learning. Experiments show that this method can accurately predict the power consumption of each type of users in different time periods, and its prediction results have a high interval coverage.

Prediction of Protein-DNA Interface Hot Spots Based on Empirical Mode Decomposition and Machine Learning.

Protein-DNA complex interactivity plays a crucial role in biological activities such as gene expression, modification, replication and transcription. Understanding the physiological significance of protein-DNA binding interfacial hot spots, as well as the development of computational biology, depends on the precise identification of these regions. In this paper, a hot spot prediction method called EC-PDH is proposed. First, we extracted features of these hot spots' solid solvent-accessible surface area (ASA) and secondary structure, and then the mean, variance, energy and autocorrelation function values of the first three intrinsic modal components (IMFs) of these conventional features were extracted as new features via the empirical modal decomposition algorithm (EMD). A total of 218 dimensional features were obtained. For feature selection, we used the maximum correlation minimum redundancy sequence forward selection method (mRMR-SFS) to obtain an optimal 11-dimensional-feature subset. To address the issue of data imbalance, we used the SMOTE-Tomek algorithm to balance positive and negative samples and finally used cat gradient boosting (CatBoost) to construct our hot spot prediction model for protein-DNA binding interfaces. Our method performs well on the test set, with AUC, MCC and F1 score values of 0.847, 0.543 and 0.772, respectively. After a comparative evaluation, EC-PDH outperforms the existing state-of-the-art methods in identifying hot spots.

Analysis of reflective cracking in asphalt overlaid jointed concrete airfield pavements using a 3D generalized finite element approach

ABSTRACT Asphalt Concrete (AC) overlays are a common strategy for maintaining PCC airfield pavements. However, the movement of joints in the underlying pavement may lead to reflective cracks propagating to the overlay. The problem is highly three-dimensional resulting in mixed mode of cracks due to the aircraft gear loading configurations and underlying concrete pavement joint spacing and design. The development of a 3D model of airfield pavements to predict reflective cracking using Finite Element Method (FEM) and Generalized Finite Element Method (GFEM), is presented in this study. GFEM enrichment strategy and global-local approach are used to achieve efficient framework for developing the models from both computational and user time perspective. Viscoelastic fracture analysis was performed using elastic-viscoelastic correspondence principle. Sensitivity of the domain size, order of approximation and boundary conditions are investigated in the numerical simulations. It is shown that the crack initiation and propagation is a combination of Mode-I (tensile) and Mode-II (shearing) crack opening. The presented models contributes to the mechanistic empirical reflective cracking design algorithm for the FAA pavement design program.

Attention mechanism-aided model ensemble method of chiller energy consumption prediction

The chiller energy consumption prediction plays an important role in reducing the building energy consumption. Continuously improving the performance of the prediction model is the key to ensuring the accuracy of the chiller energy consumption prediction. As a result, a new improved ensemble model of the chiller energy consumption prediction is proposed, based on the idea of algorithmic ensemble aided by an attention mechanism (AM). To guarantee that the proposed model can see information from many spatial and structural viewpoints, the improved ensemble model incorporates the benefits of several base prediction methods. The suggested approach chooses crucial features to simplify the model input by utilizing AM, thereby reducing the complexity of the samples. The improved ensemble model was experimentally verified in an actual multi-chiller system, and the results showed that the established model could obtain good prediction results. Finally, the proposed model was compared with existing, well-known prediction models. To evaluate the accuracy of these models, the mean absolute error (MAE), mean absolute percentage error (MAPE), mean square error (MSE), variance accounted for (VAF), and coefficient of determination (R2) were used as the evaluation indices. With an MAE of 2.858, an MAPE of 1.2755 %, an MSE of 20.1878, a VAF of 93.6058, and an R2 of 0.9923, the findings demonstrate that the improved ensemble model has a greater prediction accuracy than the other well-known prediction methods. The suggested model enhances the variety of empirical model algorithm libraries.